Network service recommendation method and apparatus

ABSTRACT

The present disclosure discloses a network service recommendation method and apparatus, which belong to a network data analysis technology. The method includes: retrieving, according to a historical browsing record of a user during use of a network service, a label corresponding to each network service used by the user; determining, according to a preset label-topic correspondence, and by using the label corresponding to each network service used by the user, first n topics corresponding to the user; acquiring, according to a preset topic-network service correspondence, respective corresponding recommended network service lists of the first n topics, the recommended network service list of each topic including at least one network service; and recommending a network service to the user according to the respective corresponding recommended network service lists of the first n topics.

FIELD OF THE TECHNOLOGY

The present disclosure relates to the network data analysistechnologies, and in particular, to a network service recommendationmethod and apparatus.

BACKGROUND OF THE DISCLOSURE

With the development of the network era, network services prevail inpeople's daily lives, and network services at least include: onlinevideos, online music, online news, and online shopping.

Online videos are used as an example. Current video recommendationstrategies include: an association rule (AR) mining strategy and aCollaborative Filtering (CF) strategy. It is assumed in both AR and CFthat an entire user group has same movie watching interest. When a videois recommended to one user, first n videos of a same type that arewatched by other users are recommended to the user, where N>1, and N isan integer. For example, because it is assumed that movie watchinginterest of an entire user group is action movies, when a video isrecommended to user A, first 10 movies in action movies watched byothers are recommended to user A.

During implementation of the present disclosure, the inventor finds thatthe foregoing technology at least has the following problem: During anactual operation, each user has different subjective interest in anetwork service, and a network service recommended according to aninterest standard of an entire user group does not necessarily meetinterest of a single user in a network service, so that an accuracy rateof whether a network service, recommended by a backend system accordingto an interest standard of an entire user group, meets interest of auser in a network service is reduced.

SUMMARY

To solve a problem that an accuracy rate of recommending a networkservice to a single user is reduced because a backend system recommendsa network service to a single user according to an interest standard ofan entire user group, embodiments of the present invention provide anetwork service recommendation method and apparatus. The technicalsolutions are as follows:

According to a first aspect of the present disclosure, a network servicerecommendation method is provided, the method including:

retrieving, according to a historical browsing record of a user duringuse of a network service, a label corresponding to each network serviceused by the user;

determining, according to a label-topic correspondence, and by using thelabel corresponding to each network service used by the user, first ntopics corresponding to the user, the first n topics being top n topicsaccording to a descending order of browsing probability of the user, andn being a positive integer;

acquiring, according to a topic-network service correspondence,respective corresponding recommended network service lists of the firstn topics, the recommended network service list of each topic includingat least one network service; and

recommending a network service to the user according to the respectivecorresponding recommended network service lists of the first n topics.

According to a second aspect of the present disclosure, a networkservice recommendation apparatus is provided, the apparatus including:

a retrieval module, configured to retrieve, according to a historicalbrowsing record of a user during use of a network service, a labelcorresponding to each network service used by the user;

a topic determination module, configured to determine, according to alabel-topic correspondence, and by using the label that is retrieved bythe retrieval module and corresponds to each network service used by theuser, first n topics corresponding to the user, the first n topics beingtop n topics according to a descending order of browsing probability ofthe user, and n being a positive integer;

an acquisition module, configured to acquire, according to atopic-network service correspondence, respective correspondingrecommended network service lists of the first n topics determined bythe topic determination module, the recommended network service list ofeach topic including at least one network service; and

a recommendation module, configured to recommend a network service tothe user according to the respective corresponding recommended networkservice lists, of the first n topics, acquired by the acquisitionmodule.

The beneficial effects brought by the technical solutions in provided inthe embodiments of the present invention are:

First n topics corresponding to a user are obtained according to ahistorical browsing record of the user, where the first n topics are topn topics according to a descending order of browsing probability of theuser, and can reflect interest of the user during use of a networkservice; by using recommended network service lists corresponding to thefirst n topics, a network service is further recommended to a useraccording to recommended network service lists corresponding to thefirst n topics; a problem that an accuracy rate of recommending anetwork service to a single user is reduced because a backend systemrecommends a network service to a single user according to an intereststandard of an entire user group is solved; and an accuracy rate ofrecommending a network service to a single user is increased.

BRIEF DESCRIPTION OF THE DRAWINGS

To describe the technical solutions in the embodiments of the presentinvention more clearly, drawings required in description of theembodiments will be introduced simply in the following. It is obviousthat the drawings in the following description are only some of theembodiments of the present invention, and a person of ordinary skill inthe art may obtain other drawings based on the drawings without creativeefforts.

FIG. 1 is a method flowchart of a network service recommendation methodprovided in an embodiment of the present invention;

FIG. 2 is a method flowchart of a network service recommendation methodprovided in another embodiment of the present invention;

FIG. 3 is a diagram of an output effect of a topic generation modelprovided in another embodiment of the present invention;

FIG. 4 is a method flowchart of another network service recommendationmethod provided in another embodiment of the present invention;

FIG. 5 is a method flowchart of still another network servicerecommendation method provided in another embodiment of the presentinvention;

FIG. 6 is a method flowchart of yet another network servicerecommendation method provided in another embodiment of the presentinvention;

FIG. 7 is a structural block diagram of a network service recommendationapparatus provided in an embodiment of the present invention;

FIG. 8 is a structural block diagram of a network service recommendationapparatus provided in another embodiment of the present invention; and

FIG. 9 is a schematic structural diagram of a server provided in anembodiment of the present invention.

DESCRIPTION OF EMBODIMENTS

To make the objectives, technical solutions, and advantages in thepresent invention clearer, the following further describes theimplementation manners of the present invention in detail with referenceto the accompanying drawings.

In the network service recommendation methods provided in theembodiments of the present invention, a network service at leastincludes: an online video, online music, online reading, and onlineshopping. A video in the online video may be: a movie, a televisionprogram, a music video (MV), a microcinema video, or a video uploaded bya netizen. The online reading may be: browsing of news and onlinereading of novels. An online video is mainly used as an example fordescription below.

Referring to FIG. 1, FIG. 1 is a method flowchart of a network servicerecommendation method provided in an embodiment of the presentinvention. The network service recommendation method includes:

Step 101: Retrieve, according to a historical browsing record of a userduring use of a network service, a label corresponding to each networkservice used by the user.

Step 102: Determine, according to a preset label-topic correspondence,and by using the label corresponding to each network service used by theuser, first n topics corresponding to the user.

The first n topics are top n topics according to a descending order ofbrowsing probability of the user, n being a positive integer.

Step 103: Acquire, according to a preset topic-network servicecorrespondence, respective corresponding recommended network servicelists of the first n topics.

The recommended network service list of each topic includes at least onenetwork service.

Step 104: Recommend a network service to the user according to therespective corresponding recommended network service lists of the firstn topics.

In conclusion, for the network service recommendation method provided inthis embodiment, first n topics corresponding to a user are obtainedaccording to a historical browsing record of the user, where the first ntopics are top n topics according to a descending order of browsingprobability of the user, and can reflect interest of the user during useof a network service; by using recommended network service listscorresponding to the first n topics, a network service is furtherrecommended to a user according to recommended network service listscorresponding to the first n topics; a problem that an accuracy rate ofrecommending a network service to a single user is reduced because abackend system recommends a network service to a single user accordingto an interest standard of an entire user group is solved; and anaccuracy rate of recommending a network service to a single user isincreased.

The network service recommendation method provided in the embodiment ofthe present invention mainly includes 2 processes:

first, a preprocessing process of topic mining, where:

a server obtains, by using the preprocessing process:

1. a topic-label correspondence, and a probability that each labelbelongs to a corresponding topic; and

2. a topic-network service correspondence, and a probability that eachnetwork service belongs to a corresponding topic; and

second, a process of recommending a network service by using ahistorical browsing record of a user and two mined correspondences.

That is, the server performs the process of recommending a networkservice by using the historical browsing record of the user and thetopic-label correspondence and the topic-network service correspondencethat are mined in advance. For details, reference may be made to theembodiment in FIG. 2:

Referring to FIG. 2, FIG. 2 is a method flowchart of a network servicerecommendation method provided in another embodiment of the presentinvention. In this embodiment, an example in which the network servicerecommendation method is applied to a server is used as an example fordescription. The network service recommendation method includes:

Step 201: Retrieve a label sequence of each network service in advance.

The label sequence of each network service includes at least one labelcorresponding to the network service.

The network service here is described by using an online video as anexample. A server retrieves a label corresponding to an online videowatched by each user, and obtains a label sequence of each online videocorresponding. The online video includes: a movie, a television program,an MV, a microcinema video or a video uploaded by a netizen.

For example, the online video is a movie. The movie may be: CaptainAmerica, Lock, Stock and Two Smoking Barrels, Running Out of Time,Infernal Affairs, and A Simple Life, and a label sequence correspondingto each movie may be:

Captain America { Chris Evans, action};

Lock, Stock and Two Smoking Barrels {Jason Statham, comedy};

Running Out of Time {Andy Lau, action};

Infernal Affairs {Andy Lau, action}; and

A Simple Life {Andy Lau, drama}.

The label in the label sequence here includes: a lead role, a moviegenre, a director, a production region, and a language. In addition, thelabel in the label sequence may further include, but is not limited to,movie watching experience, a role skill, a public comment, a plotattraction, an audio/visual attraction, and a well-known role that wasplayed by each lead role. For example, the label in the label sequenceof the movie Infernal Affairs may include: {Andy Lau, action, AndrewLau, Hongkong (China), Cantonese, Chinese, movie watching experience:the struggle between us and the enemy, a role skill: an undercoveragent, a public comment: plots are well connected, a plot attraction:the struggle between the police and the gangsters, an audio/visualattraction: great splicing of pictures and great background music, and alead role that was played by Andy Lau: Zhao Erhu in The Warlords}.

The content in the label sequence provided in the embodiment of thepresent disclosure is not specifically limited, as long as the contentcan be used to implement the network service recommendation method.

Step 202: Input the label sequence of each network service in a topicgeneration model, to obtain a label-topic correspondence and atopic-network service correspondence.

That the server inputs the label sequence of each network service in atopic generation model, to obtain a label-topic correspondence and atopic-network service correspondence specifically includes:

Step 202 a: The server inputs the label sequence of each network servicein a latent Dirichlet allocation (LDA) model, to obtain a label-topicprobability matrix and a topic-network service probability matrix.

The label-topic probability matrix includes at least one topic, a labelcorresponding to each topic, and a probability that each label belongsto a corresponding topic.

The topic-network service probability matrix includes at least onetopic, a network service corresponding to each topic, and a probabilitythat each network service belongs to a corresponding topic.

Step 202 b: The server generates the label-topic correspondenceaccording to the label-topic probability matrix.

Step 202 c: The server generates the topic-network servicecorrespondence according to the topic-network service probabilitymatrix.

For example, referring to FIG. 3, the label sequences of the moviesCaptain America. Lock, Stock and Two Smoking Barrels, Infernal Affairs,Running Out of Time, and A Simple Life in Step 201 are input in thetopic generation model. As shown on the left side of the arrow in FIG.3, the label sequences of the movies Captain America, Lock, Stock andTwo Smoking Barrels, Running Out of Time, Infernal Affairs, and A SimpleLife and the corresponding movies are represented in the form of a “6*5”matrix. That is, the rows in the matrix separately represent the labels:Chris Evans, Jason Statham, Andy Lau, comedy, action, and drama; and thecolumns of the matrix separately represent the movies: Captain America,Lock, Stock and Two Smoking Barrels, Running Out of Time, InfernalAffairs, and A Simple Life. The matrix is input in the topic generationmodel to be divided into two matrices, a “6*4” matrix A and a “4*5”matrix B on the right side of the arrow in FIG. 3.

The matrix A includes: at least one topic, a label corresponding to eachtopic, and a probability that each label corresponds to a topic. Thatis, the rows of the matrix A separately represent labels: the labels inthe label sequences corresponding to the movies in Step 201, and thecolumns of the matrix A separately represent mined topics: topic1 totopic4. The data in the matrix A is a probability of a topiccorresponding to each label.

The matrix B includes: at least one topic, an online video correspondingto each topic, and a probability that each online video belongs to acorresponding topic. That is, the rows of the matrix B separatelyrepresent topics: topic1 to topic4. The columns of the matrix Bseparately represent movies: Captain America, Lock, Stock and TwoSmoking Barrels, Running Out of Time, Infernal Affairs, and A SimpleLife, and the data in the matrix B is a probability that each onlinevideo belongs to a corresponding topic.

In the matrix shown on the left side of the arrow in FIG. 3 here, thevalue of the movie corresponding to each label represents whether thelabel is a label in the corresponding movie, if yes, it is marked as 1;and if not, it is marked as 0. A distribution rule of the probabilityvalues in the matrix A is: a sum of probability values of the topicscorresponding to the labels in the row is 1, and a distribution rule ofthe probability values in the matrix B is: a sum of the probabilityvalues that each movie belongs to a corresponding topic in the columnis 1. The probability values in the matrix A and matrix B are adjustedaccording to the number of the topics, so as to ensure that in thematrix A, a sum of probabilities in each row is finally 1, and in thematrix B, a sum of probabilities of each column is finally 1. Theprobabilities of the matrix A and the matrix B may be determinedaccording to duration of browsing or the number of times of browsing,and are adjusted according to the number of mined topics. The mined“topic” may be regarded as a class mined by using an algorithm.

The “at least one topic, a label corresponding to each topic, and aprobability that each label belongs to a corresponding topic” and the“at least one topic, a network service corresponding to each topic, anda probability that each network service belongs to a correspondingtopic” provided in the embodiment of the present invention arerepresented, for example, by the matrix A and the matrix B obtainedabove, and are not specifically limited, as long as the networkrecommendation method provided in the present disclosure is implemented.

In the embodiment provided in the present disclosure, the label sequenceof each network service is input in the topic generation model, that is,at least one topic, a label corresponding to each topic, and aprobability that each label belongs to a corresponding topic, and atleast one topic, a network service corresponding to each topic, and aprobability that each network service belongs to a corresponding topicare obtained based on an LDA and according to a relationship betweeneach movie and a topic.

Because each label may belong to many topics, for simplification,optionally:

Step 203: Perform, if one label belongs to two or more topics in alabel-topic probability matrix, arrangement according to a descendingorder of probability that a label belongs to each topic, and keep firstS topics as topics to which the label belongs, S being a positiveinteger; and generate the label-topic correspondence according to thelabel and the first S topics.

Here the matrix A shown on the right side of the arrow in Step 202 inFIG. 3 is used as an example for description. The topics to which thelabel in the matrix A belongs are arranged according to a descendingorder of probability, and first 2 topics are kept as topics to which thelabel belongs, thereby obtaining that the label “Chris Evans” belongs tothe topics topic1 (the corresponding probability is 0.4) and topic2 (thecorresponding probability is 0.3), the label “Andy Lau” belongs to thetopics topic1 (the corresponding probability is 0.4) and topic2 (thecorresponding probability is 0.3), the label “Jason Statham” belongs tothe topics topic2 (the corresponding probability is 0.4) and topic4 (thecorresponding probability is 0.3), the label “action” belongs to thetopics topic1 (the corresponding probability is 0.3) and topic3 (thecorresponding probability is 0.4), the label “comedy” belongs to thetopics topic1 (the corresponding probability is 0.3) and topic4 (thecorresponding probability is 0.4), and the label “drama” belongs to thetopics topic2 (the corresponding probability is 0.3) and topic4 (thecorresponding probability is 0.4).

It is obtained from the above that the label-topic correspondence is asfollows:

The topic topic1 includes: “Chris Evans”, “Andy Lau”, “action”, and“comedy”;

The topic topic2 includes: “Chris Evans”, “Andy Lau”, “Jason Statham”,and “drama”;

The topic topic3 includes: “action”; and

The topic topic4 includes: “Jason Statham”, “comedy”, and “drama”.

Similarly, because each network service may belong to many topics, forsimplification, optionally:

Optionally, Step 204: Perform, if one network service belongs to two ormore topics in the topic-network service probability matrix, arrangementaccording to a descending order of probability that a network servicebelongs to each topic, and keep first M topics as topics to which thenetwork service belongs, where M≧1 and M is an integer; and generate thetopic-network service correspondence according to the network serviceand the first M topics to which the network service belongs.

Here, the matrix B shown on the right side of the arrow in FIG. 3 inStep 202 is used as an example for description. Arrangement is performedaccording to a descending order of probability that a network servicebelongs to each topic in the matrix B, and first 2 topics are kept astopics to which the network service belongs, thereby obtaining that, themovie Captain America belongs to the topics topic1 (the correspondingprobability is 0.4) and topic3 (the corresponding probability is 0.3),the movie Lock, Stock and Two Smoking Barrels belongs to the topicstopic2 (the corresponding probability is 0.4) and topic4 (thecorresponding probability is 0.3), the movie Running Out of Time belongsto the topics topic1 (the corresponding probability is 0.3) and topic3(the corresponding probability is 0.4), the movie Infernal Affairsbelongs to the topics topic3 (the corresponding probability is 0.3) andtopic4 (the corresponding probability is 0.4), and the movie A SimpleLife belongs to the topics topic1 (the corresponding probability is 0.4)and topic3 (the corresponding probability is 0.3).

It is obtained from the above that the topic-network servicecorrespondence is as follows:

The topic topic1 includes: Captain America, Running Out of Time, and ASimple Life;

The topic topic2 includes: Lock, Stock and Two Smoking Barrels;

The topic topic3 includes: Captain America, Running Out of Time,Infernal Affairs, and A Simple Life; and

The topic topic4 includes: Lock, Stock and Two Smoking Barrels, andInfernal Affairs.

Here Step 203 and Step 204 are based on Step 202, and the number oftopics corresponding to each label and the number of topicscorresponding to each network service obtained in Step 202 areseparately selected according to a descending order of probabilityvalue, so that data obtained in Step 203 and Step 204, as compared withthe data in Step 202 that has not been arranged in order and selected,can further reflect interest and preference of a user when the userwatches movies.

Step 205: Calculate a recommendation degree of each network serviceaccording to a preset parameter.

The preset parameter includes: at least one of a probability that thenetwork service belongs to a corresponding topic, the number of times ofbrowsing corresponding to the network service, a public rating of thenetwork service, and duration that the network service has beenreleased.

Specifically, that the server calculates a recommendation degree of eachnetwork service according to a preset parameter is as follows:

For example, the server acquires in advance at least one of a topic towhich each movie belongs, a probability corresponding to a topic towhich each movie belongs, the number of times of browsing correspondingto the network service, a public rating of the network service, andduration that the network service has been released. That is, withreference to Step 204, the server acquires in advance that CaptainAmerica, Running Out of Time, and A Simple Life belong to the topictopic1, and the corresponding probabilities are: Captain America 0.4,Running Out of Time 0.3, and A Simple Life 0.4; the server acquires inadvance that Lock, Stock and Two Smoking Barrels belongs to the topictopic2, and the corresponding probability is 0.4; the server acquires inadvance that Captain America, Running Out of Time, Infernal Affairs, andA Simple Life belong to the topic topic3, and the correspondingprobabilities are: Captain America 0.3, Running Out of Time 0.4,Infernal Affairs, 0.3, and A Simple Life 0.3; and the server acquires inadvance that Lock, Stock and Two Smoking Barrels and Infernal Affairsbelong to the topic topic4, and the corresponding probabilities are:Lock, Stock and Two Smoking Barrels 0.3 and Infernal Affairs 0.4.

In topic 1:

The number of times that Captain America is browsed is 8, the publicrating is 6.2, and the release duration is: 3 years (released on Sep. 9,2011).

The number of times that is Running Out of Time browsed is 8, the publicrating is 8.3, and the release duration is: 15 years (released on Sep.23, 1999).

The number of times that A Simple Life is browsed is 7, the publicrating is 7.0, and the release duration is: 2 years (released on Mar. 8,2012).

In topic2:

The number of times that Lock, Stock and Two Smoking Barrels is browsedis 10, the public rating is 9.1, and the release duration is: 16 years(released on Aug. 28, 1998).

In topic3:

The number of times that Captain America is browsed is 8, the publicrating is 6.2, and the release duration is: 3 years (released on Sep. 9,2011).

The number of times that Running Out of Time is browsed is 8, the publicrating is 8.3, and the release duration is: 15 years (released on Sep.23, 1999).

The number of times that A Simple Life is browsed is 7, the publicrating is 7.0, and the release duration is: 2 years (released on Mar. 8,2012).

The number of times that Infernal Affairs is browsed is 10, the publicrating is 8.8, and the release duration is: 12 years (released on Dec.12, 2002).

In topic4:

The number of times that Lock, Stock and Two Smoking Barrels is browsedis 10, the public rating is 9.1, and the release duration is: 16 years(released on Aug. 28, 1998).

The number of times that Infernal Affairs is browsed is 10, the publicrating is 8.8, and the release duration is: 12 years (released on Dec.12, 2002).

It is assumed that a time attenuation factor is set according to releaseduration, so that for a movie within 10 years, release duration isreduced to 10% according to the release duration, and for a movie over10 years, release duration is reduced to 1% according to the releaseduration.

The recommendation degree of each movie in topic1 :

The recommendation degree of Captain America is: 0.4*8*6.2*0.3=5.952.

The recommendation degree of Running Out of Time is:0.3*8*8.3*0.15=2.988.

The recommendation degree of A Simple Life is: 0.4*7*7.0*0.2=3.92.

The recommendation degree of each movie in topic2:

The recommendation degree of Lock, Stock and Two Smoking Barrels is:0.4*10*9.1*0.16=5.824.

The recommendation degree of each movie in topic3:

The recommendation degree of Captain America is: 0.3*8*6.2*0.3=4.464.

The recommendation degree of Running Out of Time is:0.4*8*8.3*0.15=3.984.

The recommendation degree of Infernal Affairs is: 0.3*10*8.8*0.12=3.168.

The recommendation degree of A Simple Life is: 0.3*7*7.0*0.2=2.94.

The recommendation degree of each movie in topic4:

The recommendation degree of Lock, Stock and Two Smoking Barrels is:0.3*10*9.1*0.16=4.368.

The recommendation degree of Infernal Affairs is: 0.4*10*8.8*0.12=4.224.

The public rating of the network service here may be obtained throughstatistics according to a rating result of each user after moviewatching, or may be obtained through commenting by professionals, whichis not limited in the embodiment provided in the present disclosure.

Step 206: Arrange an order of network services in a recommended networkservice list corresponding to each topic according to a recommendationdegree.

Arrange an order according to the recommendation degree of each movie inthe topic topic1, to obtain the recommended network service listcorresponding to topic1, as shown in Table 1:

TABLE 1 Recommendation Rank Movie degree 1 Captain America 5.952 2 ASimple Life 3.92 3 Running Out of Time 2.988

As shown in Table 1, the movie Captain America ranks the first in thetopic topic1, and the recommendation degree is 5.952; the movie A SimpleLife ranks the second in the topic topic1, and the recommendation degreeis 3.92; and the movie Running Out of Time ranks the third in the topictopic1, and the recommendation degree is 2.988.

Arrange an order according to the recommendation degree of each movie inthe topic topic2, to obtain the recommended network service listcorresponding to topic2, as shown in Table 2:

TABLE 2 Recommendation Rank Movie degree 1 Lock, Stock and Two SmokingBarrels 5.824

As shown in Table 2, the movie Lock, Stock and Two Smoking Barrels ranksthe first in the topic topic2, and the recommendation degree is 5.824.

Arrange an order according to the recommendation degree of each movie inthe topic topic3, to obtain the recommended network service listcorresponding to topic3, as shown in Table 3:

TABLE 3 Recommendation Rank Movie degree 1 Captain America 4.464 2Running Out of Time 3.984 3 Infernal Affairs 3.168 4 A Simple Life 2.94

As shown in Table 1, the movie Captain America ranks the first in thetopic topic3, and the recommendation degree is 4.464; the movie RunningOut of Time ranks the second in the topic topic3, and the recommendationdegree is 3.984; the movie Infernal Affairs ranks the third in the topictopic3, and the recommendation degree is 3.168; and the movie A SimpleLife ranks the fourth in the topic topic3, and the recommendation degreeis 2.94.

Arrange an order according to the recommendation degree of each movie intopic topic4, to obtain the recommended network service listcorresponding to topic4, as shown in Table 4:

TABLE 4 Recommendation Rank Movie degree 1 Lock, Stock and Two SmokingBarrels 4.368 2 Infernal Affairs 4.224

As shown in Table 4, the movie Lock, Stock and Two Smoking Barrels ranksthe first in the topic topic4, and the recommendation degree is 4.368;and the movie Infernal Affairs ranks the second in the topic topic4, andthe recommendation degree is 4.224.

Here Steps 201 to 206 are a preprocessing process when the serverrecommends a corresponding movie to each user. It should be noted that,the foregoing process does not need to be performed once in everyrecommendation process, and only needs to be completed before arecommendation process, for example, is performed before arecommendation service is provided, or, is performed once everypredetermined time interval.

A process of recommending a network service to a user by using twocorrespondences obtained in advance is provided below:

Step 207: Retrieve, according to a historical browsing record of a userduring use of a network service, a label corresponding to each networkservice used by the user.

Specifically, that the server retrieves, according to a historicalbrowsing record of a user during use of a network service, a labelcorresponding to each network service used by the user may include:

a. The server determines a network service, meeting an effectivebrowsing condition, in the historical browsing record.

The effective browsing condition includes that: browsing durationexceeds predetermined duration, and/or, the number of times of browsingexceeds a predetermined number of times.

For example, the server selects a movie that is watched by the user andexceeds 20 minutes as a movie that meets the effective browsingcondition.

For another example, the server selects a movie that the user haswatched more than 3 times as a movie that meets the effective browsingcondition.

In this embodiment, that browsing duration exceeds predeterminedduration is used as a condition to select a network service meeting aneffective browsing condition, which is, however, not specificallylimited.

b. The server retrieves a label corresponding to the network servicemeeting the effective browsing condition.

Here, the server retrieves a historical browsing record when a userwatches online videos, where online videos in the historical browsingrecord are, for example, Blind Detective, Firestorm, Captain America 2,and You Are the Apple of My Eye, and the retrieved labels correspondingto the movies may be:

Blind Detective {Andy Lau, action};

Firestorm {Andy Lau, action};

Captain America 2 {Chris Evans, action}; and

You Are the Apple of My Eye {Ko Chen-tung, drama}.

Step 208: Determine, according to a preset label-topic correspondence,and by using the label corresponding to each network service used by theuser, first n topics corresponding to the user.

The first n topics are top n topics according to a descending order ofbrowsing probability of the user, n being a positive integer.

Here that the server determines, according to a preset label-topiccorrespondence, and by using the label corresponding to each networkservice used by the user, first n topics corresponding to the userincludes:

Step 208 a: The server queries a topic corresponding to each label fromthe label-topic correspondence.

The label-topic correspondence includes: a correspondence between eachlabel and each topic, and a probability that each label belongs to acorresponding topic.

For example, the preset label-topic correspondence may be the matrix Ashown in FIG. 3, and a topic corresponding to each label when a userbrowses online videos may be obtained through querying, that is:

Blind Detective {Andy Lau, action};

where the label “Andy Lau” belongs to the topics topic1 and topic2, and“action” belongs to the topics topic1 and topic3;

Firestorm {Andy Lau, action};

where the label “Andy Lau” belongs to the topics topic1 and topic2, and“action” belongs to the topics topic1 and topic3;

Captain America 2 {Chris Evans, action};

where the label “Chris Evans” belongs to the topics topic1 and topic2,and “action” belongs to the topics topic1 and topic3; where the label“Andy Lau” here corresponds to a probability value of 0.4 in the topictopic1 ; the label “Andy Lau” corresponds to a probability value of 0.3in the topic topic2; “Chris Evans” belongs to the topic topic1, andcorresponds to a probability value of 0.4; “Chris Evans” belongs to thetopic topic2, and corresponds to a probability value of 0.3; the label“action” corresponds to a probability value of 0.3 in the topic topic1 ;and “action” corresponds to a probability value of 0.4 in the topictopic3; and

You Are the Apple of My Eye {Ko Chen-tung, drama}

where the label “drama” corresponds to a probability value of 0.3 in thetopic topic2; and “drama” corresponds to a probability value of 0.4 inthe topic topic4.

Step 208 b: The server adds, for each found topic, probabilitiescorresponding to labels that belong to a same topic, to obtain aprobability value of the topic.

According to the topic corresponding to each label found in Step 208 a,probabilities corresponding to labels that belong to the topic topic1are added to obtain that the probability of the topic topic1 is 2.1,that is:

the label “Andy Lau” in Blind Detective belongs to topic1, and theprobability value corresponding to “Andy Lau” is 0.4; the label “action”belongs to topic1, and the probability value corresponding to “action”is 0.3;

the label “Andy Lau” in Firestorm belongs to topic1, and the probabilityvalue corresponding to “Andy Lau” is 0.4; the label “action” belongs totopic1, and the probability value corresponding to “action” is 0.3; and

the label “Chris Evans” in Captain America 2 belongs to topic1, and theprobability value corresponding to “Chris Evans” is 0.4; the label“action” belongs to topic1, and the probability value corresponding to“action” is 0.3.

Therefore, the server adds the probabilities corresponding to the labelsthat belong to the topic topic1 to obtain:

0.4+0.4+0.4+0.3+0.3+0.3=2.1.

Probabilities corresponding to labels that belong to the topic topic2are added to obtain that the probability of the topic topic2 is 2.4,that is:

the label “Andy Lau” in Blind Detective belongs to topic2, and theprobability value corresponding to “Andy Lau” is 0.3; the label “action”belongs to topic2, and the probability value corresponding to “action”is 0.4;

the label “Andy Lau” in Firestorm belongs to topic2, and the probabilityvalue corresponding to “Andy Lau” is 0.3; the label “action” belongs totopic2, and the probability value corresponding to “action” is 0.4; and

the label “Chris Evans” in Captain America 2 belongs to topic2, and theprobability value corresponding to “Chris Evans” is 0.3; the label“action” belongs to topic2, and the probability value corresponding to“action” is 0.4; and

the label “drama” in You Are the Apple of My Eye belongs to topic2, andthe probability value corresponding to “drama” is 0.3.

Therefore, the server adds the probabilities corresponding to the labelsthat belong to the topic topic2 to obtain:

0.3+0.4+0.3+0.4+0.3+0.4+0.3=2.4.

Probabilities corresponding to labels that belong to the topic topic3are added, to obtain that the probability of the topic topic3 is 1.2,that is:

the label “action” in Blind Detective belongs to topic3, and theprobability value corresponding to “action” is 0.4;

the label “action” in Firestorm belongs to topic3, and the probabilityvalue corresponding to “action” is 0.4; and

the label “action” in Captain America 2 belongs to topic3, and theprobability value corresponding to “action” is 0.4.

Therefore, the server adds the probabilities corresponding to the labelsthat belong to the topic topic3 to obtain:

0.4+0.4+0.4=1.2.

Probabilities corresponding to labels that belong to the topic topic4are added to obtain that the probability of the topic topic4 is 0.4,that is:

the label “drama” in You Are the Apple of My Eye belongs to topic4, andthe probability value corresponding to “drama” is 0.4.

Therefore, the server adds the probabilities corresponding to the labelsthat belong to the topic topic4 to obtain that: 0.4.

Step 208 c: The server arranges the topics according to a descendingorder of probability value, to obtain first n topics corresponding tothe user.

Here, the probability values corresponding to the topics topic1, topic2,topic3, and topic topic4 are arranged in a descending order according tothe results in Step 208 b to obtain that:

first, the probability value of topic2 is 2.4;

second, the probability value of topic1 is 2.1;

third, the probability value of topic3 is 1.2; and

fourth, the probability value of topic4 is 0.4.

It is set that n is 3, and it is obtained from the above that the first3 topics corresponding to the user are the topics topic1, topic2, andtopic3.

Step 209: Acquire, according to a preset topic-network servicecorrespondence, respective corresponding recommended network servicelists of the first n topics.

The recommended network service list of each topic includes at least onenetwork service.

With reference to the first n topics corresponding to the user obtainedin Step 208 c, that is, topic1, topic2, and topic3, and according toStep 206, it is obtained through querying that a recommended networkservice list corresponding to topic1 Table 1, a recommended networkservice list corresponding to topic2 is Table 2, and a recommendednetwork service list corresponding to topic3 is Table 3.

Step 210: Recommend a network service to the user according to therespective corresponding recommended network service lists of the firstn topics.

Here network services to be recommended to the user are obtainedaccording to Step 209, where the network services are, in therecommended network service list corresponding to the topic topic1, themovie Captain America that ranks the first, the movie A Simple Life thatranks the second, and the movie Running Out of Time that ranks thethird; in the recommended network service list corresponding to thetopic topic2, the movie Lock, Stock and Two Smoking Barrels that ranksthe first; and in the recommended network service list corresponding tothe topic topic3, the movie Captain America that ranks the first, themovie Running Out of Time that ranks the second, and the movie InfernalAffairs that ranks the third.

Alternatively, the server recommends to the user the movies that rankthe first N in the recommended network service list, where N>1 and N isan integer.

For example,

respective corresponding recommended network service lists of 3 topicsare obtained according to Step 206, and in Step 209, the movies thatrank the first 3 in the recommended network service lists correspondingto each topic may be recommended to the user according to the respectivecorresponding recommended network service lists of the 3 topics, thatis, Captain America, A Simple Life, Lock, Stock and Two Smoking Barrels,Running Out of Time, and Infernal Affairs are obtained.

Here the number of selected topics, the number of movies in therecommended network service list corresponding to each topic, and firstN movies selected from each recommended network service list aredescribed by using an example of implementation of the networkrecommendation method provided in the embodiment of the presentinvention, and are not specifically limited.

Here, a recommendation process of recommending a network service to auser may be completed.

As another possible implementation manner, referring to FIG. 4, analternative method for Step 203 may be:

Step 203 a: Perform, one label belongs to two or more topics in thelabel-topic probability matrix, arrangement according to a descendingorder of probability that a label belongs to each topic, and keep atopic whose probability is greater than a preset threshold as a topicthat the label belongs to; and generate a label-topic correspondenceaccording to the label and the first S topics.

Here the matrix A shown on the right side of the arrow in Step 202 inFIG. 3 is used as an example for description. It is set that thethreshold is 0.3, a topic whose probability is greater than 0.3 is takenas a topic to which a label belongs, and therefore it is obtainedaccording to the matrix A in FIG. 3 that the label “Chris Evans” belongsto the topic topic1 (the corresponding probability is 0.4), the label“Andy Lau” belongs to the topic topic1 (the corresponding probability is0.4), the label “Jason Statham” belongs to the topic topic2 (thecorresponding probability is 0.4), the label “action” belongs to thetopic topic3 (the corresponding probability is 0.4), the label “comedy”belongs to the topic topic4 (the corresponding probability is 0.4), andthe label “drama” belongs to the topic topic4 (the correspondingprobability is 0.4).

It is obtained from the above that the label-topic correspondence is:

The topic topic1 includes: “Chris Evans” and “Andy Lau”.

The topic topic2 includes: “Jason Statham”.

The topic topic3 includes: “action”.

The topic topic4 includes: “comedy” and “drama”.

Compared with Step 203, the number of labels distributed in each topicin Step 203 a is more even than the number of labels distributed in eachtopic in Step 203, and a case in which multiple labels gather at a fewtopics is avoided.

As another possible implementation manner, referring to FIG. 5, analternative method for Step 204 is:

Step 204 a: Perform, if one network service belongs to two or moretopics in the topic-network service probability matrix, arrangementaccording to a descending order of probability that a network servicebelongs to each topic, and keep a topic whose probability is greaterthan a preset threshold as a topic to which the network service belongs;and generate the topic-network service correspondence according to thenetwork service and first M topics to which the network service belongs.

Here the matrix B shown on the right side of the arrow in FIG. 3 in Step202 is used as an example for description. It is set that the thresholdis 0.3, a topic whose probability is greater than 0.3 is taken as atopic to which a network service belongs, and therefore it is obtainedaccording to the matrix B shown in FIG. 3 that, the movie CaptainAmerica belongs to the topic topic1 (the corresponding probability is0.4), the movie Lock, Stock and Two Smoking Barrels belongs to the topictopic2 (the corresponding probability is 0.4), the movie Running Out ofTime belongs to topic3 (the corresponding probability is 0.4), the movieInfernal Affairs belongs to topic4 (the corresponding probability is0.4), and the movie A Simple Life belongs to the topic topic1 (thecorresponding probability is 0.4).

It is obtained from the above that the topic-network servicecorrespondence is:

The topic topic1 includes: Captain America and A Simple Life.

The topic topic2 includes: Lock, Stock and Two Smoking Barrels.

The topic topic3 includes: Running Out of Time.

The topic topic4 includes: Infernal Affairs.

Compared with Step 204, the number of movies distributed in each topicin Step 204 a is more even than the number of movies distributed in eachtopic in Step 204, and a case in which multiple movies gather at a fewtopics is avoided.

Here, in addition to the methods separately shown in FIG. 4 and FIG. 5,in the solution provided in the embodiment of the present invention,Step 203 a and Step 204 a may also be combined to implement the networkrecommendation method provided in the present disclosure.

A schematic flowchart of the method of Step 201 to Step 210 in theembodiment provided in the present disclosure may be shown in FIG. 6.

Similarly, when a television program is recommended to a user, atelevision program corresponding to interest of the user can also beprovided for the user according to the network service recommendationmethod provided in the embodiment of the present invention, andspecifics are no longer provided.

When a user does online shopping, the number of times of browsingcorresponding to multiple commodities may be acquired according to arecord of browsed commodities, and results same as those of movierecommendation provided in the embodiment of the present invention mayfurther be obtained by using each label corresponding to each commodityand a relationship between each label and each commodity. The case isthe same with online reading, and is no longer elaborated here.

In conclusion, in the network service recommendation method provided inthis embodiment, first n topics corresponding to a user are obtainedaccording to a historical browsing record of the user, where the first ntopics are top n topics according to a descending order of browsingprobability of the user, and can reflect interest of the user during useof a network service; by using recommended network service listscorresponding to the first n topics, a network service is furtherrecommended to a user according to recommended network service listscorresponding to the first n topics; a problem that an accuracy rate ofrecommending a network service to a single user is reduced because abackend system recommends a network service to a single user accordingto an interest standard of an entire user group is solved; and anaccuracy rate of recommending a network service to a single user isincreased.

Moreover, by using a set threshold, a topic whose probability is greaterthan a preset threshold is kept as a topic to which a network servicebelongs, so that it is avoided that multiple network services gather ata few topics.

Referring to FIG. 7, FIG. 7 is a structural block diagram of a networkservice recommendation apparatus provided in an embodiment of thepresent invention. The network service recommendation apparatusincludes: a retrieval module 310, a topic determination module 320, anacquisition module 330, and a recommendation module 340.

The retrieval module 310 is configured to retrieve, according to ahistorical browsing record of a user during use of a network service, alabel corresponding to each network service used by the user;

The topic determination module 320 is configured to determine, accordingto a preset label-topic correspondence, and by using the label that isretrieved by the retrieval module 310 and corresponds to each networkservice used by the user, first n topics corresponding to the user, thefirst n topics being top n topics according to a descending order ofbrowsing probability of the user, and n being a positive integer.

The acquisition module 330 is configured to acquire, according to apreset topic-network service correspondence, respective correspondingrecommended network service lists of the first n topics determined bythe topic determination module 320, the recommended network service listof each topic including at least one network service.

The recommendation module 340 is configured to recommend a networkservice to the user according to the respective correspondingrecommended network service lists, of the first n topics, acquired bythe acquisition module 330.

In conclusion, in the network service recommendation apparatus providedin this embodiment, first n topics corresponding to a user are obtainedaccording to a historical browsing record of the user, where the first ntopics are top n topics according to a descending order of browsingprobability of the user, and can reflect interest of the user during useof a network service; by using recommended network service listscorresponding to the first n topics, a network service is furtherrecommended to a user according to recommended network service listscorresponding to the first n topics; a problem that an accuracy rate ofrecommending a network service to a single user is reduced because abackend system recommends a network service to a single user accordingto an interest standard of an entire user group is solved; and anaccuracy rate of recommending a network service to a single user isincreased.

Referring to FIG. 8, FIG. 8 is a structural block diagram of a networkservice recommendation apparatus provided in another embodiment of thepresent invention. The network service recommendation apparatusincludes: a retrieval module 310, a topic determination module 320, anacquisition module 330, a recommendation module 340, a sequenceretrieval module 350, a generation module 360, an operational module370, and an ordering module 380.

The sequence retrieval module 350 is configured to retrieve a labelsequence of each network service in advance, the label sequence of eachnetwork service including at least one label corresponding to thenetwork service.

The generation module 360 is configured to input, the label sequence, ofeach network service, retrieved by the sequence retrieval module 350, ina topic generation model, to obtain a label-topic correspondence and atopic-network service correspondence.

Optionally, the generation module 360 includes:

a decomposition unit 361, configured to input the label sequence of eachnetwork service in a topic generation model, for example, an LDA, toobtain a label-topic probability matrix and a topic-network serviceprobability matrix, the label-topic probability matrix including atleast one topic, a label corresponding to each topic, and a probabilitythat each label belongs to a corresponding topic; and the topic-networkservice probability matrix including at least one topic, a networkservice corresponding to each topic, and a probability that each networkservice belongs to a corresponding topic; and

a first generation unit 362, configured to generate the label-topiccorrespondence according to the label-topic probability matrix obtainedby the decomposition unit 361.

Furthermore, the first generation unit 362 is configured to perform, ifone label belongs to two or more topics in the topic-network serviceprobability matrix, arrangement according to a descending order ofprobability that a label belongs to each topic, and keep first S topicsas topics to which the label belongs, S being a positive integer; andgenerate the label-topic correspondence according to the label and thefirst S topics;

or,

the first generation unit 362 is configured to perform, if one labelbelongs to two or more topics in the topic-network service probabilitymatrix, arrangement according to a descending order of probability thatthe label belongs to each topic, and keep a topic whose probability isgreater than a preset threshold as a topic to which the label belongs;and generate the label-topic correspondence according to the label andthe first S topics.

The second generation unit 363 is further configured to generate thetopic-network service correspondence according to the topic-networkservice probability matrix obtained by the decomposition unit 361.

Furthermore, the second generation unit 363 is configured to perform, ifone network service belongs to two or more topics in the topic-networkservice probability matrix, arrangement according to a descending orderof probability that the network service belongs to each topic, and keepfirst M topics as topics to which the network service belongs, where M>1and M is an integer; and generate the topic-network servicecorrespondence according to the network service and the first M topicsto which the network service belongs;

or,

the second generation unit 363 is configured to perform, if one networkservice belongs to two or more topics in the topic-network serviceprobability matrix, arrangement according to a descending order ofprobability that a network service belongs to each topic, and keep atopic whose probability is greater than a preset threshold as a topic towhich a network service belongs; and generate the topic-network servicecorrespondence according to the network service and the first M topicsto which the network service belongs.

The retrieval module 310 is configured to retrieve, according to ahistorical browsing record of a user during use of a network service, alabel corresponding to each network service used by the user.

Optionally, the retrieval module 310 includes:

a filtration unit 311, configured to determine a network service,meeting an effective browsing condition, in the historical browsingrecord, the effective browsing condition including that: browsingduration exceeds predetermined duration, and/or, the number of times ofbrowsing exceeds a predetermined number of times; and

a label retrieval unit 312, configured to retrieve a label correspondingto the network service meeting the effective browsing condition.

The topic determination module 320 is configured to determine, accordingto a preset label-topic correspondence, and by using the label that isretrieved by the retrieval module 310 and corresponds to each networkservice used by the user, first n topics corresponding to the user, thefirst n topics being top n topics according to a descending order ofbrowsing probability of the user, and n being a positive integer.

Optionally, the topic determination module 320 includes:

a query unit 321, configured to query a topic corresponding to eachlabel from a label-topic correspondence, the label-topic correspondenceincluding: a correspondence between each label and each topic, and aprobability that each label belongs to a corresponding topic;

an adding unit 322, configured to add, for each topic found by the queryunit 321, probabilities corresponding to labels that belong to thetopic, to obtain the probability value of the topic; and

an ordering unit 323, configured to arrange each topic according to adescending order of probability value, to obtain first n topicscorresponding to the user.

The operational module 370 is configured to calculate a recommendationdegree of each network service according to a preset parameter, thepreset parameter including: at least one of a probability that thenetwork service belongs to a corresponding topic, the number of times ofbrowsing corresponding to the network service, a public rating of thenetwork service, and duration that the network service has beenreleased.

The ordering module 380 is configured to arrange, according to therecommendation degree, an order of network services in the recommendednetwork service list corresponding to each topic.

The acquisition module 330 is configured to acquire, according to apreset topic-network service correspondence, respective correspondingrecommended network service lists, of the first n topics, determined bythe topic determination module 320, the recommended network service listof each topic including at least one network service.

The recommendation module 340 is configured to recommend a networkservice to the user according to the respective correspondingrecommended network service lists, of the first n topics, acquired bythe acquisition module 330.

In conclusion, in the network service recommendation apparatus providedin this embodiment, first n topics corresponding to a user are obtainedaccording to a historical browsing record of the user, where the first ntopics are top n topics according to a descending order of browsingprobability of the user, and can reflect interest of the user during useof a network service; by using recommended network service listscorresponding to the first n topics, a network service is furtherrecommended to a user according to recommended network service listscorresponding to the first n topics; a problem that an accuracy rate ofrecommending a network service to a single user is reduced because abackend system recommends a network service to a single user accordingto an interest standard of an entire user group is solved; and anaccuracy rate of recommending a network service to a single user isincreased.

Moreover, a threshold is set to keep a topic whose probability isgreater than a preset threshold as a topic to which a network servicebelongs, so that it is avoided that multiple network services gather ata few topics.

Referring to FIG. 9, FIG. 9 is a schematic structural diagram of aserver provided in an embodiment of the present invention. The server400 includes a central processing unit (CPU) 401, a system memory 404including a random access memory (RAM) 402 and a read-only memory (ROM)403, and a system bus 405 connecting the system memory 404 and the CPU401. The server 400 further includes a basic input/output (I/O) system406 for helping transmission of information between devices in acomputer, and a massive storage device 407 configured to store anoperating system 413, an application program 410, and other programmodules 415.

The basic input/output system 406 includes a display 408 configured todisplay information and an input device 409 such as a mouse and akeyboard configured to input information by a user. The display 408 andthe input device 409 are both connected to an input/output controller410 of the system bus 405 to be connected to the CPU 401. The basicinput/output system 406 may further include an input/output controller410 configured to receive and process input from multiple other devicessuch as a keyboard, a mouse or an electronic stylus. Similarly, theinput/output controller 410 further provides output to a display screen,a printer or an output device of another type.

The massive storage device 407 is connected to a massive storagecontroller (not shown) of the system bus 405 to be connected to the CPU401. The massive storage device 407 and its related computer readablemedium provide the server 400 with non-volatile storage. That is, themassive storage device 407 may include a computer readable medium (notshown) such as a hard drive or a CD-ROM drive.

Without loss of generality, the computer readable media include computerstorage media and communications media. The computer storage mediainclude volatile and non-volatile, and removable and non-removable mediaimplemented in any method or technology for storage of information suchas computer-readable instructions, data structures, program modules orother data. The computer storage media include, but are not limited to,a RAM, a read-only memory (ROM), an electrically erasable programmableROM (EEPROM), a flash memory or other solid-state memory technologies,compact disc ROM (CD-ROM), a digital versatile disk (DVD) or otheroptical storage devices, and a magnetic cassette, a magnetic tape, amagnetic disk storage device or other magnetic storage devices.Certainly, the person of ordinary skill in the art may know that thecomputer storage medium is not limited to the foregoing types. Thesystem memory 404 and the massive storage device 407 may be generallyreferred to as a memory.

According to various embodiments of the present invention, the server400 may further run on a remote computer connected to a network by usinga network such as the Internet. That is, the server 400 may be connectedto a network 412 by using a network interface unit 411 connected to thesystem bus 405, or may also be connected to a network or a remotecomputer system (not shown) of another type by using a network interfaceunit 411.

The memory may further include one or more programs. The one or moreprograms are stored in the memory. The processor is configured toperform, according to the programs stored in the memory, the foregoingnetwork service recommendation method.

The sequence numbers of the above embodiments of the present inventionare merely for the convenience of description, and do not imply thepreference among the embodiments.

A person of ordinary skill in the art may understand that all or some ofthe steps of the foregoing embodiments may be implemented by usinghardware, or may be implemented by a program instructing relevanthardware. The program may be stored in a computer readable storagemedium. The storage medium may be a ROM, a magnetic disk, an opticaldisc, or the like.

The foregoing descriptions are merely preferred embodiments of thepresent invention, but are not intended to limit the present invention.Any modification, equivalent replacement, or improvement made within thespirit and principle of the present invention shall fall within theprotection scope of the present invention.

1. A network service recommendation method, wherein the methodcomprises: retrieving, according to a historical browsing record of auser during use of a network service, a label corresponding to eachnetwork service used by the user; determining, according to alabel-topic correspondence, and by using the label corresponding to eachnetwork service used by the user, first n topics corresponding to theuser, the first n topics being top n topics according to a descendingorder of browsing probability of the user, and n being a positiveinteger; acquiring, according to a topic-network service correspondence,respective corresponding recommended network service lists of the firstn topics, the recommended network service list of each topic comprisingat least one network service; and recommending a network service to theuser according to the respective corresponding recommended networkservice lists of the first n topics.
 2. The method according to claim 1,before the retrieving, according to a historical browsing record of auser during use of a network service, a label corresponding to eachnetwork service used by the user, further comprising: retrieving a labelsequence of each network service in advance, the label sequence of eachnetwork service comprising at least one label corresponding to thenetwork service; inputting the label sequence of each network service ina topic generation model, to obtain the label-topic correspondence andthe topic-network service correspondence.
 3. The method according toclaim 2, wherein the inputting the label sequence of each networkservice in a topic generation model, to obtain the label-topiccorrespondence and the topic-network service correspondence comprises:inputting the label sequence of each network service in a latentDirichlet allocation (LDA) model, to obtain a label-topic probabilitymatrix and a topic-network service probability matrix, the label-topicprobability matrix comprising at least one topic, a label correspondingto each topic, and a probability that each label belongs to acorresponding topic; and the topic-network service probability matrixcomprising at least one topic, a network service corresponding to eachtopic, and a probability that each network service belongs to acorresponding topic; generating the label-topic correspondence accordingto the label-topic probability matrix; and generating the topic-networkservice correspondence according to the topic-network serviceprobability matrix.
 4. The method according to claim 3, wherein thegenerating the label-topic correspondence according to the label-topicprobability matrix comprises: performing, if one label belongs to two ormore topics in the label-topic probability matrix, arrangement accordingto a descending order of probability that the label belongs to eachtopic, and keeping first S topics as topics to which the label belongs,S being a positive integer; and generating the label-topiccorrespondence according to the label and the first S topics; or,performing, if one label belongs to two or more topics in thelabel-topic probability matrix, arrangement according to a descendingorder of probability that the label belongs to each topic, and keepingtopics whose probabilities are greater than a preset threshold as topicsto which the label belongs; and generating the label-topiccorrespondence according to the label and the first S topics.
 5. Themethod according to claim 3, wherein the generating the topic-networkservice correspondence according to the topic-network serviceprobability matrix comprises: performing, if one network service belongsto two or more topics in the topic-network service probability matrix,arrangement according to a descending order of probability that thenetwork service belongs to each topic, and keeping first M topics astopics to which the network service belongs, M being a positive integer;and generating the topic-network service correspondence according to thenetwork service and the first M topics to which the network servicebelongs; or, performing, if one network service belongs to two or moretopics in the topic-network service probability matrix, arrangementaccording to a descending order of probability that the network servicebelongs to each topic, and keeping topics whose probabilities aregreater than a preset threshold as topics to which the network servicebelongs; and generating the topic-network service correspondenceaccording to the network service and the first M topics to which thenetwork service belongs.
 6. The method according to claim 1, wherein theretrieving, according to a historical browsing record of a user duringuse of a network service, a label corresponding to each network serviceused by the user comprises: determining a network service, meeting aneffective browsing condition, in the historical browsing record, theeffective browsing condition comprising that: browsing duration exceedspredetermined duration, and/or, the number of times of browsing exceedsa predetermined number of times; and retrieving a label corresponding tothe network service meeting the effective browsing condition.
 7. Themethod according to claim 1, wherein the determining, according to alabel-topic correspondence, and by using the label corresponding to eachnetwork service used by the user, first n topics corresponding to theuser comprises: querying a topic corresponding to each label from thelabel-topic correspondence, the label-topic correspondence comprising: acorrespondence between each label and each topic, and a probability thateach label belongs to a corresponding topic; adding, for each foundtopic, probabilities corresponding to labels that belong to the topic,to obtain a probability value of the topic; and arranging an order ofeach topic according to a descending order of probability value, toobtain the first n topics corresponding to the user.
 8. The methodaccording to claim 1, before the acquiring, according to a topic-networkservice correspondence, respective corresponding recommended networkservice lists of the first n topics, further comprising: calculating arecommendation degree of each network service according to a presetparameter, the preset parameter comprising: at least one of aprobability that the network service belongs to a corresponding topic,the number of times of browsing corresponding to the network service, apublic rating of the network service, and duration that the networkservice has been released; and arranging an order, according to therecommendation degree, of a network service in the recommended networkservice list corresponding to each topic.
 9. A network servicerecommendation apparatus, wherein the apparatus comprises: a retrievalmodule, configured to retrieve, according to a historical browsingrecord of a user during use of a network service, a label correspondingto each network service used by the user; a topic determination module,configured to determine, according to a label-topic correspondence, andby using the label that is retrieved by the retrieval module andcorresponds to each network service used by the user, first n topicscorresponding to the user, the first n topics being top n topicsaccording to a descending order of browsing probability of the user, andn being a positive integer; an acquisition module, configured toacquire, according to a topic-network service correspondence, respectivecorresponding recommended network service lists of the first n topicsdetermined by the topic determination module, the recommended networkservice list of each topic comprising at least one network service; anda recommendation module, configured to recommend a network service tothe user according to the respective corresponding recommended networkservice lists, of the first n topics, acquired by the acquisitionmodule.
 10. The apparatus according to claim 9, wherein the apparatusfurther comprises: a sequence retrieval module, configured to retrieve,before the label corresponding to each network service used by the useris retrieved according to the historical browsing record of the userduring use of the network service, a label sequence of each networkservice in advance, the label sequence of each network servicecomprising at least one label corresponding to the network service; anda generation module, configured to input the label sequence, of eachnetwork service, retrieved by the sequence retrieval module, in a topicgeneration model, to obtain the label-topic correspondence and thetopic-network service correspondence.
 11. The apparatus according toclaim 10, wherein the generation module comprises: a decomposition unit,configured to input the label sequence of each network service in alatent Dirichlet allocation (LDA) model, to obtain a label-topicprobability matrix and a topic-network service probability matrix, thelabel-topic probability matrix comprising at least one topic, a labelcorresponding to each topic, and a probability that each label belongsto a corresponding topic; and the topic-network service probabilitymatrix comprising at least one topic, a network service corresponding toeach topic, and a probability that each network service belongs to acorresponding topic; a first generation unit, configured to generate thelabel-topic correspondence according to the label-topic probabilitymatrix obtained by the decomposition unit; and a second generation unit,further configured to generate the topic-network service correspondenceaccording to the topic-network service probability matrix obtained bythe decomposition unit.
 12. The apparatus according to claim 11,wherein, the first generation unit is configured to perform, if onelabel belongs to two or more topics in the label-topic probabilitymatrix, arrangement according to a descending order of probability thatthe label belongs to each topic, and keep first S topics as topics towhich the label belongs, S being a positive integer; and generate thelabel-topic correspondence according to the label and the first Stopics; or, the first generation unit is configured to perform, if onelabel belongs to two or more topics in the label-topic probabilitymatrix, arrangement according to a descending order of probability thatthe label belongs to each topic, and keep topics whose probabilities aregreater than a preset threshold as topics to which the label belongs;and generate the label-topic correspondence according to the label andthe first S topics.
 13. The apparatus according to claim 11, wherein,the second generation unit is configured to perform, if one networkservice belongs to two or more topics in the topic-network serviceprobability matrix, arrangement according to a descending order ofprobability that the network service belongs to each topic, and keepfirst M topics as topics to which the network service belongs, whereinM≧1 and M is an integer; and generate the topic-network servicecorrespondence according to the network service and the first M topicsto which the network service belongs; or, the second generation unit isconfigured to perform, if one network service belongs to two or moretopics in the topic-network service probability matrix, arrangementaccording to a descending order of probability that the network servicebelongs to each topic, and keep topics whose probabilities are greaterthan a preset threshold as topics to which the network service belongs;and generate the topic-network service correspondence according to thenetwork service and the first M topics to which the network servicebelongs.
 14. The apparatus according to claim 9any one of claim 9,wherein the retrieval module comprises: a filtration unit, configured todetermine a network service, meeting an effective browsing condition, inthe historical browsing record, the effective browsing conditioncomprising that: browsing duration exceeds predetermined duration,and/or, the number of times of browsing exceeds a predetermined numberof times; and a label retrieval unit, configured to retrieve a labelcorresponding to the network service meeting the effective browsingcondition.
 15. The apparatus according to claim 9, wherein the topicdetermination module comprises: a query unit, configured to query atopic corresponding to each label from the label-topic correspondence,the label-topic correspondence comprising: a correspondence between eachlabel and each topic, and a probability that each label belongs to acorresponding topic; an adding unit, configured to add, each topic foundby the query unit, probabilities corresponding to labels that belong tothe topic, to obtain a probability value of the topic; and an orderingunit, configured to arrange an order of each topic according to adescending order of probability value, to obtain the first n topicscorresponding to the user.
 16. The apparatus according to claim 9,wherein the apparatus further comprises: an operational module,configured to calculate, before respective corresponding recommendednetwork service lists of the first n topics are acquired according tothe topic-network service correspondence, a recommendation degree ofeach network service according to a preset parameter, the presetparameter comprising: at least one of a probability that the networkservice belongs to a corresponding topic, the number of times ofbrowsing corresponding to the network service, a public rating of thenetwork service, and duration that the network service has beenreleased; and an ordering module, configured to arrange an order,according to the recommendation degree, of a network service in therecommended network service list corresponding to each topic.